作　　者： (李继猛）; (张云刚）; (张金凤）; (谢平）;

机构地区： 燕山大学电气工程学院,河北秦皇岛066004

出　　处： 《计量学报》 2017年第5期602-606,共5页

摘　　要： 针对强背景噪声下冲击信号难以检测的问题,提出一种基于自适应随机共振的齿轮微弱冲击故障信号增强提取方法。首先,利用峭度指标和互相关系数构造修正峭度指标作为随机共振检测冲击信号的测度函数;其次,利用滑动窗将多冲击分量信号分割成多个单冲击分量信号作为随机共振的系统输入,并借助遗传算法实现系统参数的自适应选取;最后,将提出的方法应用于电力机车走行部齿轮箱故障诊断,结果显示该方法可有效实现微弱冲击特征的增强提取。 Aiming at the problem of impact signal detection under strong noise background, an adaptive stochastic resonance method for enhancement and extraction of gear weak impact Fault Signal is proposed. First, a new modified kurtosis index is constructed by using kurtosis index and correlation coefficient, which is applied as the measurement index of stochastic resonance for the detection of impact signals. Second, a data segmentation algorithm via sliding window is adopted to segment the impact signal with different impact amplitudes into multiple sub-signals with single impact component, which are used as the system input of stochastic resonance. And the genetic algorithm is employed to realize the adaptive selection of system parameters. Finally, the proposed method is applied to gearbox fault diagnosis of traveling unit of electric locomotive. The results show that this method can effectively extract the features of gear fault.

关 键 词： 计量学 冲击特征提取 随机共振 滑动窗 修正峭度指标 齿轮箱故障诊断